• The Journal of the Korea Contents Association
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• v.15 no.5
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• pp.488-497
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• 2015

The recent increase of chonsei has raised the degree of lease burden of households, and a new residential lease price index needs to be introduced to measure such degree of lease burden. In order to convert the burden into an index, the calculation method of the K-HAI, which is announced by the Korea Housing Financing Corporation, is applied by replacing house purchase with lease. From the calculation, the residential lease prices index of the first quarter of 2014 is estimated to be approximately 114, indicating that the cost of lease exceeds 35% of income. The result of analysis on the trend of the residential lease prices index from the first quarter of 2012 to the present in Seoul indicates that the residential lease prices index in Seoul has continued to increase, compared to that of the entire country. The results of this study will be a foundation to find a solution for the stabilization of chonsei and investigate the degree of lease burden by region when establishing a sustainable housing policy.

• Korean Journal of Construction Engineering and Management
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• v.17 no.3
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• pp.108-115
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• 2016

The price determinant for land investment depends on the internal information process and subjective decision making by management in general. Accordingly, the systematic frame to determine the feasibility of investment price to the public residential land for multi-housing development by private sector has not been proposed. The purpose of this study is to explore the frame to determine the investment price for public residential land from the perspectives of land attribute and apartment market factor. Multiple regression has been implemented to confirm the eligibility of proposed model. Research findings indicate that the land area, floor area ratio, coverage ratio, location have been identified as the total land cost determinant, and for the determinants for floor area land cost, the ratio of apartment, sale price, rent price, etc, have been identified. This research intends to provide the basis for land providers to predict the land value as a raw material in market and present the indicators for land buyers to review the price adequacy for the investment.

• Proceeding of Spring/Autumn Annual Conference of KHA
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• 2011.04a
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• pp.285-289
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• 2011

he purpose of this study is to assess the effects of real estate policy on apartment price index in Seoul. To meet the research goal, this research reviewed real estate policy of the government from January of 1986 to August of 2010, and then it collected monthly apartment price index in 25 local districts of Seoul from January of 2003 to August of 2010. After 25 districts were grouped into 2 areas (14 districts in Gangnam and 11 districts in Gangbuk), the data of two areas were analyzed by using the SAS program, Cluster analysis with Ward method showed 3 clusters on each area, and with 6 clusters in total, the effects of real estate policy in the period were examined by using residual analysis. The analysis indicated two major shocks (one was from May to October of 2003, and the other was from March of 2006 to January of 2007), and the results showed that the intervention of government in the market had the asymmetric effects in bullish and bearish times. It implies that the market volatility is substantially influenced by irrational sentiments. Thus, it's suggested to devise the consumer sentiment index suitable in real estate market.

• Journal of the Economic Geographical Society of Korea
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• v.13 no.4
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• pp.515-528
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• 2010

This study analyzes the impacts of mixed-use development and transportation on housing values in Seoul, Korea. An index measuring the land use mix is proposed using three components of land uses, residence, office, and retail, which are the essential elements for everyday urban life. This index offers a relatively easy way in measuring the level of mixed-use and proves itself useful providing sensible and reliable results in this empirical study. Also surface and underground transportation accessibilities are measured. By covering both surface and underground, a comprehensive view of Seoul's transportation accessibility is provided. Finally, housing value models are constructed with developed variables, i.e. land use mix index and accessibility measures, as well as relevant socio-economic variables. The empirical outcomes verifies that mixed-use development and transportation accessibility positively affect housing values.

• Korea Real Estate Review
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• v.27 no.3
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• pp.71-86
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• 2017

The purpose of this study was to apply the deep running method to real estate price index predicting and to compare it with the time series analysis method to test the possibility of its application to real estate market forecasting. Various real estate price indices were predicted using the DNN (deep neural networks) and LSTM (long short term memory networks) models, both of which draw on the deep learning method, and the ARIMA (autoregressive integrated moving average) model, which is based on the time seies analysis method. The results of the study showed the following. First, the predictive power of the deep learning method is superior to that of the time series analysis method. Second, among the deep learning models, the predictability of the DNN model is slightly superior to that of the LSTM model. Third, the deep learning method and the ARIMA model are the least reliable tools for predicting the housing sales prices index among the real estate price indices. Drawing on the deep learning method, it is hoped that this study will help enhance the accuracy in predicting the real estate market dynamics.

• Smart Media Journal
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• v.11 no.3
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• pp.62-73
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• 2022

Researches on the prediction of domestic apartment sales price have been continuously conducted, but it is not easy to accurately predict apartment prices because various characteristics are compounded. Prior to predicting apartment sales price, the analysis of major factors, influencing on sale prices, is of paramount importance to improve the accuracy of sales price. Therefore, this study aims to analyze what are the factors that affect the apartment sales price in Gwangju, which is currently showing a steady increase rate. With 6 years of Gwangju apartment transaction price and various social factor data, several maching learning techniques such as multiple regression analysis, random forest, and deep artificial neural network algorithms are applied to identify major factors in each model. The performances of each model are compared with RMSE (Root Mean Squared Error), MAE (Mean Absolute Error) and R² (coefficient of determination). The experiment shows that several factors such as 'contract year', 'applicable area', 'certificate of deposit', 'mortgage rate', 'leading index', 'producer price index', 'coincident composite index' are analyzed as main factors, affecting the sales price.

• Land and Housing Review
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• v.12 no.2
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• pp.33-45
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• 2021

Due to recent changes in government policy, officetels have received attention as alternative assets, along with the uplift of office and apartment prices in Seoul. However, the current officetel price indexes use small-size samples and, thus, there is a critique on their accuracy. They rely on valuation prices which lag the market trend and do not properly reflect the volatile nature of the property market, resulting in 'smoothing'. Therefore, the purpose of this paper is to create the officetel price index using transaction data. The data, provided by the Ministry of Land, Infrastructure and Transport from 2005 to 2020, includes sales prices and rental prices - Jeonsei and monthly rent (and their combinations). This study employed a repeat sales model for sales, jeonsei, and monthly rent indexes. It also contributes to improving conversion rates (between deposit and monthly rent) as a supplementary indicator. The main findings are as follows. First, the officetel price index and jeonsei index reached 132.5P and 163.9P, respectively, in Q4 2020 (1Q 2011=100.0P). However, the rent index was approximately below 100.0. Sales prices and jeonsei continued to rise due to high demand while monthly rent was largely unchanged due to vacancy risk. Second, the increase in the officetel sales price was lower than other housing types such as apartments and villas. Third, the employed approach has seen a potential to produce more reliable officetel price indexes reflecting high volatility compared to those indexes produced by other institutions, contributing to resolving 'smoothing'. As seen in the application in Seoul, this approach can enhance accuracy and, therefore, better assist market players to understand the market trend, which is much valuable under great uncertainties such as COVID-19 environments.

• The Transactions of the Korea Information Processing Society
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• v.13 no.5
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• pp.209-216
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• 2024

Real estate market prices are determined by various factors, including macroeconomic variables, as well as the influence of a variety of unstructured text data such as news articles and social media. News articles are a crucial factor in predicting real estate transaction prices as they reflect the economic sentiment of the public. This study utilizes sentiment analysis on news articles to generate a News Sentiment Index score, which is then seamlessly integrated into a real estate price prediction model. To calculate the sentiment index, the content of the articles is first summarized. Then, using AI, the summaries are categorized into positive, negative, and neutral sentiments, and a total score is calculated. This score is then applied to the real estate price prediction model. The models used for real estate price prediction include the Multi-head attention LSTM model and the Vector Auto Regression model. The LSTM prediction model, without applying the News Sentiment Index (NSI), showed Root Mean Square Error (RMSE) values of 0.60, 0.872, and 1.117 for the 1-month, 2-month, and 3-month forecasts, respectively. With the NSI applied, the RMSE values were reduced to 0.40, 0.724, and 1.03 for the same forecast periods. Similarly, the VAR prediction model without the NSI showed RMSE values of 1.6484, 0.6254, and 0.9220 for the 1-month, 2-month, and 3-month forecasts, respectively, while applying the NSI led to RMSE values of 1.1315, 0.3413, and 1.6227 for these periods. These results demonstrate the effectiveness of the proposed model in predicting apartment transaction price index and its ability to forecast real estate market price fluctuations that reflect socio-economic trends.

• Journal of the Economic Geographical Society of Korea
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• v.25 no.1
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• pp.171-181
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• 2022

The real estate price index plays key roles as quantitative data in real estate market analysis. International organizations including OECD publish the real estate price indexes by country, and the Korea Real Estate Board announces metropolitan-level and municipal-level indexes. However, when the index is set on the smaller spatial unit level than metropolitan and municipal-level, problems occur: missing values. As the spatial scope is narrowed down, there are cases where there are few or no transactions depending on the unit period, which lead index calculation difficult or even impossible. This study suggests a supervised learning-based machine learning model to compensate for missing values that may occur due to no transaction in a specific range and period. The models proposed in our research verify the accuracy of predicting the existing values and missing values.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.2
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• pp.205-213
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• 2010

This study was to analyze the past regime's real estate policy and the time-series data on real estate price index from 1986 to 2009 in 24 years. Also, the real estate index and macroeconomic variables, the impact on house price index variable conducted to regression analysis and to analyze whether and how much is affected. Analyzed as follows: First, Korea's real estate policy was the post-policy and the past regime's real estate policy was inconsistent with each other. Second, in the normal phase whenever real estate issues, the measures of the strengthening regulation and of the economic recovery were only to repeat periodically. Third, the timing and means of policy enforcement was an inappropriate and Real estate market was getting worse at the time whenever a real estate policies performed. Fourth, The apartments prices index of the housing types rose the highest and were the most popular for 24 years. Increase or decrease the amount of the price index for apartments, Roh Tae-woo(65.0%) - Kim Dae-jung (42.5%) - Roh Moo-hyun (32.8%) were in order. Fifth, the results of the regression analysis carried out: The impact on housing prices among independent variables were followed by Cap Construction- one per capita income - Housing consumer price index - Accompanying Composite Index - Trailing Composite Index - Home subscription Subscriber account - Leading Composite Index.